Database Systems for Advanced Applications

.- Recommendation.
.- Hypergraph Enhanced Knowledge Tree Prompt Learning for Next-Basket Recommendation.
.- MHGCP:Multi-View Heterogeneous Graph with Cross-View Projection for Recommendation.
.- Towards Scenario-adaptive User Behavior Modeling for Multi-scenario Recommendation.
.- Leave No One Behind: Enhancing Diversity While Maintaining Accuracy in Social Recommendation.
.- Counterfactual Path Augmentation for Reinforcement Reasoning in Explainable Recommendation.
.- Adaptive Personalized Federated Recommendation with Global Knowledge Distillation.
.- FHCF: Fully-Hyperbolic Symmetric Graph Learning for Collaborative Filtering.
.- UGDA: A Unified Graph-based Method with Domain-specific Adaptation for Multi-domain Recommendation.
.- Self-supervised Hierarchical Representation for Medication Recommendation.
.- Self-Supervised Dual Graph and Intention Association for Session-based Recommendation.
.- Exercise Recommendation Based on Feature-Aligned Knowledge Tracing.
.- Joint User and Item Prototype Alignment for Cross-Platform Recommendation.
.- Diffusion Multi-Behavior Recommender Model .
.- HHGCN-DrugRec: Hierarchical HyperGraph Convolution Network for Drug Combination Recommendation.
.- Emotion-based Conversational Recommendation by Inferring Implicit Users' Preferences from their Subjective Claims.
.- CDIVR: Cognitive Dissonance-aware Interactive Video Recommendation.
.- Modeling Personalized Short-term and Periodic Long-term Preferences for Enhanced Next POI Recommendations.
.- DRE: Generating Recommendation Explanations by Aligning Large Language Models at Data-level.
.- Towards Unified Modeling for Positive and Negative Preferences in Sign-aware Recommendation.
.- Alignment-Uniformity Aware Feature Representation Learning for CTR Prediction.
.- Diffusion Based Data Augmentation for Multi-behavior Sequential Recommendation.
.- Semantic Gaussian Mixture Variational Autoencoder for Sequential Recommendation*.
.- Personalized Education with Ranking Alignment Recommendation.
.- HierLLM: Hierarchical Large Language Model for Question Recommendation.
.- Comprehensive Interest Modeling and Relational Mining for Multi-modal Recommendation.
.- Demand-oriented Route Recommendation for Shared Mobility Services.
.- CoCoB: Adaptive Collaborative Combinatorial Bandits for Online Recommendation.
.- KG-TS: Knowledge Graph-driven Thompson Sampling for Online Recommendation.
.- Efficient Noise-reducing Neural Network for Cross-Domain Sequential Recommendation.
.- Bridging RDF Knowledge Graphs with Graph Neural Networks for Semantically-Rich Recommender Systems.
.- Security & Privacy.
.- Lattice-based Forward Secure Certificateless Encryption Scheme for Cloud Data Management.
.- Logarithmic-size Lattice-based Linkable Ring Signature for Cloud Data Management.
.- CyberLLM: Enable Mapping CVE to Tactics and Techniques of Cyber Threats via LLM.
.- Privacy-preserving Multi-Dimensional Range Query Optimization Across Multiple Sources.
.- Decoupled Self-Knowledge Distillation Makes Differentially Private Deep Learning Stronger.
.- PriExRec: Defending Against Membership Inference Attacks in Federated Recommendation with Explicit Feedback.
.- OPOM: The Ordinal and Parallel Optimization Method of Spark multi-query applications.
.- Enabling Efficient and Authenticated Trajectory Similarity Retrieval on Blockchain-assisted Cloud.
.- InC: A Vertical Federated Learning Framework with Multiple Noisy Labels.
.- Breaking Free from Label Limitations: A Novel Unsupervised Attack Method for Graph Classification.
.- TSALockMark: An Asymmetric and Robust Watermarking Scheme for Relational Databases with Distortion Constraints.
.- Towards Confidential and Efficient LLM Inference with Dual Privacy Protection.
.- ECPIR: Efficient and Controllable Privacy-Preserving Image Retrieval in Cloud-Assisted System.
.- Privacy-preserving Image Generation Based on Self-Attention.